arXiv AI By Wei Sun, Tingyu Qu, Jesse Davis, Marie-Francine Moens

Event Interaction in Low-Rank Bottlenecks for Temporal Relation Extraction

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Hugging Face Trending Papers
Jun 1

WALL-WM: Carving World Action Modeling at the Event Joints

WALL-WM is a World Action Model that shifts video-action learning from chunk-centric optimization to event-grounded Vision-Language-Action pretraining, using semantically coherent action events as the atomic unit of learning. Existing WAMs commonly initialize from multimodal or video foundation models and then optimize fixed-length action chunks conditioned directly on the current observation and instruction.

arXiv Computer Vision
Aug 27

FLEET: Token-Based Feature Extraction for Event Camera-based Reinforcement Learning

FLEET is a token‑based feature extractor that processes event camera data directly, using random Fourier features and cross‑attention to compress variable‑length event streams into fixed‑size latent representations. By decoupling inference cost from sensor resolution, it avoids the high compute and temporal blurring associated with CNN‑based grid aggregation. Experiments on a new high‑throughput benchmark show that FLEET outperforms state‑of‑the‑art methods and remains robust across different observation frequencies.

By Tristan Gottwald, Maximilian Schier, Melanie Schaller, Bodo Rosenhahn